OmniGen2

OmniGen2 is an efficient multimodal generation model that combines visual language models and diffusion models, enabling functions such as visual understanding, image generation, and editing. Its open-source nature provides researchers and developers with a strong foundation to explore personalized and controllable AI generation Target Users: This product is suitable for researchers, developers, and designers who need efficient tools to generate and edit images, supporting personalized customization and innovative design. Use Cases: Generate corresponding images based on user-provided text descriptions.Use instructions to modify existing images in design work to meet requirements.Combine various input data to generate rich visual content for promotional or educational materials. Features: Visual understanding: Strong ability to analyze image content.Text-to-image generation: Generate high-quality images based on text prompts.Instruction-guided image editing: Accurately perform complex image modifications.Contextual generation: Process and combine different inputs to produce novel visual outputs.Supports multiple input formats, flexible application in different scenarios.Provides a user-friendly interface and online demo platform.Open-source code and datasets for research and development. How to Use: Clone the code repository: git clone git@github.com:VectorSpaceLab/OmniGen2.gitCreate and activate Python environment: conda create -n omnigen2 python=3.11, conda activate omnigen2Install PyTorch and other dependencies: pip install torch==2.6.0 torchvision, pip install -r requirements.txtRun the example: bash example_t2i.sh for text-to-image generation.Access the online demo or run the local application for image generation and editing.